I have one year of stock data. My 12 input is fuzzified dataMy 12 output also fuzzified data.I want to compare the result from original data with neural network result. and also i want to predict the stock data.

> What version of MATLAB do you have?Matlab version: MATLAB Version 7.10.0.499 (R2010a)> Please post the documentation you get from the command> > help newffNEWFF Create a feed-forward backpropagation network.

The transfer functions TF{i} can be any differentiable transfer function such as TANSIG, LOGSIG, or PURELIN.

The training function BTF can be any of the backprop training functions such as TRAINLM, TRAINBFG, TRAINRP, TRAINGD, etc.

*WARNING*: TRAINLM is the default training function because it is very fast, but it requires a lot of memory to run. If you get an "out-of-memory" error when training try doing one of these:

(1) Slow TRAINLM training, but reduce memory requirements, by setting NET.trainParam.mem_reduc to 2 or more. (See HELP TRAINLM.) (2) Use TRAINBFG, which is slower but more memory efficient than TRAINLM. (3) Use TRAINRP which is slower but more memory efficient than TRAINBFG.

The learning function BLF can be either of the backpropagation learning functions such as LEARNGD, or LEARNGDM.

The performance function can be any of the differentiable performance functions such as MSE or MSEREG.

The first layer has weights coming from the input. Each subsequent layer has a weight coming from the previous layer. All layers have biases. The last layer is the network output.

Each layer's weights and biases are initialized with INITNW.

Adaption is done with TRAINS which updates weights with the specified learning function. Training is done with the specified training function. Performance is measured according to the specified performance function.